Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review
To date, the application of NLP methods to clinical text for populating clinical registries has been limited in both the number of published studies and the scope of implementation. The NLP methods used thus far face significant challenges in effectively managing the complexity and diversity of clinical text and data elements. Moreover, the performance of the NLP methods varied significantly. This review underscores the need for a robust and adaptable NLP framework. Generative LLMs may provide direction for future research, but their use must account for challenges such as accuracy, cost, privacy, and limited supporting evidence.
Authors
- Tim Churches (ORCID: https://orcid.org/0000-0002-7905-5877)
- Blanca Gallego Luxan (ORCID: https://orcid.org/0000-0002-3704-7975)
- Sze‐Yuan Ooi (ORCID: https://orcid.org/0000-0001-6916-7958)
- Georgina Kennedy (ORCID: https://orcid.org/0000-0003-2331-0530)
- Geoff P. Delaney (ORCID: https://orcid.org/0000-0002-1829-396X)
- Victoria Blake (ORCID: https://orcid.org/0009-0006-7006-7130)
- Louisa Jorm (ORCID: https://orcid.org/0000-0003-0390-661X)
- Leibo Liu (ORCID: https://orcid.org/0000-0002-7517-0050)
- Matthew Barman
Institutions
- UNSW Sydney (AU)
- Prince of Wales Hospital (AU)
- Ingham Institute (AU)
- South Western Sydney Local Health District (AU)
- Western Sydney University (AU)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1093/jamia/ocaf176
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 9.92